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Springer Fuzzy Logic Neural Networks Nature-Inspired Optimization - Springer

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Description

This comprehensive volume explores cutting-edge methods for developing intelligent systems using fuzzy logic, neural networks, and nature-inspired optimisation techniques. Spanning eight main sections, the book covers theoretical foundations of fuzzy systems alongside practical applications in intelligent control, robotics, pattern recognition, time series prediction, and complex problem optimisation. The collection brings together recent advances and novel algorithms that combine fuzzy logic frameworks with neural network architectures and bio-inspired computational approaches. Ideal for researchers, engineers, and academics seeking to understand how these complementary technologies integrate to solve real-world challenges across diverse domains. Part of the prestigious Studies in Computational Intelligence series (volume 601), this work provides both conceptual depth and applied insights for advancing intelligent system design.

Key Features

Covers fuzzy logic theory with new concepts and algorithms for intelligent system design and implementation.

Explores neural network applications integrated with fuzzy systems for enhanced pattern recognition and decision-making.

Addresses nature-inspired optimisation methods for solving complex problems across multiple computational domains.

Includes practical applications in intelligent control systems, robotics, and real-time automation.

Contains sections on time series prediction using hybrid approaches combining fuzzy and neural technologies.

Organises content across eight thematic parts for systematic exploration of fuzzy logic and neural network integration.

Published by Springer as part of the Studies in Computational Intelligence series for academic credibility.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
15 October 2016
Listed Since
07 October 2016

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